Besides pinpointing microfractures, the researchers believe combining color spectral CT imaging with their novel nanoparticles could help detect more serious problems such as heart blockages.

Not only can the metamaterial boost signal-to-noise ratio 10-fold, but It can reduce scan times, potentially making the modality more widely available for patients at lower costs.

"The 7T MRI scanner affords us new ways of viewing areas of damage in neurologic diseases such as MS that were not well seen using 3T MRI," corresponding author Jonathan Zurawski, MD, said.

Coronary artery calcium scoring has proven to be more predictive of cardiovascular risk than any other biomarker, but quantifying scores via imaging remains a time-consuming and labor-intensive task.

St. George University Hospitals Foundation Trust in the U.K. admitted that missed radiology findings contributed to the death of three patients at its hospital, according to reporting from Health Services Journal.

Radiologists, medical students and surgeons all agree that AI should be incorporated into diagnostic radiology, but for the most part their perceptions of the technology are drastically different.

When we go to sleep at night, our brains are wiped clean of harmful toxins. Boston-area researchers now have the evidence to prove it, thanks in part to high-resolution imaging.

The CDC has tallied more than 2,000 cases of the illness across the U.S. so far, including 39 deaths.

Radiologists from the Netherlands believe deep learning can significantly impact cardiac MRI analysis in the not so distant future, sharing their thoughts in a piece published in the American Journal of Roentgenology.

Nearly one in five inductions of labor lead to a required emergency C-section, but current methods to predict such events are largely subjective with low predictive accuracy.

No medical specialty outside of radiology and cardiology educate their trainees in image interpretation enough to justify nonradiologist physicians reading studies in the clinical setting, according to new research published in the American Journal of Roentgenology.

Deep learning can identify cancerous and precancerous esophagus tissue on digitized pathology slides, opening the door for AI to alter the digital pathology landscape.